MD-Mamba: A multi-scale dilated state-space network for breast cancer histopathology image classification.

BACKGROUND: Automated breast histopathology classification requires models that can capture both local cellular morphology and broader tissue architecture while remaining computationally efficient. METHODS: We developed MD-Mamba, a multi-scale dilated state-space network with dual-path spatial-channel attention for four-class breast histopathology image classification. The model integrates selective state space modeling for long-range tissue organization, multi-dilated convolutions for local morphology at complementary receptive fields, and attention modules for spatial and channel-wise feature refinement. MD-Mamba was evaluated on the ICIAR 2018 BACH dataset using a stratified 80:20 split and compared with CNN, transformer, and hybrid baselines under the same protocol. RESULTS: In this benchmark split, MD-Mamba achieved 0.9625 accuracy, 0.9654 macro-precision, 0.9625 macro-recall, 0.9617 macro-F1, 0.9956 AUC, and 0.9500 Cohen's kappa. Errors were concentrated at the Normal-Benign boundary. Ablation and sensitivity analyses supported the contribution of MDCM, spatial attention, and channel attention. CONCLUSIONS: MD-Mamba showed strong benchmark performance for BACH four-class breast histopathology classification. External whole-slide, multi-institutional, patient-independent, and molecularly annotated validation is required before claims about clinical translation or biomarker utility can be made.